[Senior ML Researcher Application ]
WATER is a Biophysical Intelligence research company. Our mission is to teach matter to respond to life: we study how the human body and the physical world respond to each other, and we build machines that sense a body, model its state, and respond in real time. CAMA, an adaptive bed, and FLOW, an adaptive chair, are the first surfaces this research runs on. The work spans three research directions: machine learning and mathematics, bio research, and materials and electronics. The long-range outcomes we care about are human longevity and human experience.
You own one research line from open question to a model running on a device. You'll work on data strategy, architecture, training, evaluation, and the studies that produce your ground truth.
In This Role You Will
- -Own a research line end to end — problem framing, data strategy, architecture, training, evaluation, handoff.
- -Design the dataset before the model. Decide what must be collected, under what conditions, with what ground truth.
- -Build an evaluation protocol that survives contact with real users rather than a held-out split.
- -Train, ablate, and be honest in writing about which component actually mattered.
- -Work directly with the human-factors and sleep scientists who produce your labels. You will be in the room for study design.
- -Get your model onto constrained hardware, and stay involved when it degrades there.
- -Publish and present the work.
What We Hope You'll Bring
- -You have carried a research problem from open question to working model, and can name the three things that nearly killed it.
- -PhD, or 4+ years of research experience with a publication or shipped-model record.
- -Strong PyTorch. You write experiment code someone else can rerun.
- -Rigorous about evaluation and suspicious of your own results.
- -Comfortable on a problem where the dataset does not exist yet.
- -Nice to have: sequence and state-space models, tactile or pressure data, physiological signals (EEG, PSG, HRV, actigraphy), self-supervised representation learning, biometric identification.